Method and device for prolonging service life of transformer in inland river harbor area

Abstract

The invention discloses a method and device for prolonging the service life of a transformer in an inland river harbor district, and the method comprises the steps: collecting the real-time current and voltage data of a lithium ion battery of each electric heavy truck in a target harbor district, so as to construct a lithium ion battery dual-polarization equivalent circuit model of each electric heavy truck; discretizing the constructed dual-polarization equivalent circuit model of each lithium ion battery, and establishing a discrete state space model of each lithium ion battery, so as to obtain the real-time charge state of the battery of each electric heavy truck; based on a transformer winding hot-spot temperature calculation model, establishing an electric heavy truck charging scheduling model with minimum transformer life loss as an objective function; and solving the electric heavy truck charging scheduling model by combining the real-time state of charge of the battery of each electric heavy truck and each formulated constraint condition to obtain an optimal scheduling scheme of the charging access time and the charging duration of each electric heavy truck, so that the service life loss of the transformer is minimized, and the service life of the transformer is prolonged.

Description

Technical Field

[0001] The invention belongs to the technical field of port area power systems, and in particular relates to a method and a device for extending the service life of an inland port area transformer. Background Art

[0002] Power transformers are the most expensive power equipment in substations. They play the role of energy transmission in power systems. Their voltage conversion function makes power transformers a key device in power grids. Once a power transformer stops operating due to a fault, it will not only cause the loss of power equipment assets, but also may cause significant social and economic losses. Therefore, it is necessary to formulate a suitable maintenance strategy for power transformers.

[0003] At present, there are many studies on the technology of extending the service life of transformers. Some technologies use the three-dimensional finite element method to accurately calculate the stray losses of the structural components of the power transformer and consider the location selection of the magnetic shield, so as to minimize the total load loss of the transformer and increase the service life of the transformer. In other related technologies, it is proposed that the size of the core loss of the transformer mainly depends on the core material of the transformer. By studying the core loss of the three-phase distribution transformer and basing the results on finite element analysis, it is concluded that the use of M4 cold-rolled grain-oriented silica gel as the core material is beneficial to increase the life of the transformer. However, the above studies all focus on the onshore power system. The existing technologies rarely consider that the transformers in the port distribution system also have life maintenance needs. Since the port has a unique load and operation mode, the existing research results are not applicable to port transformers. Moreover, the above studies only extend the life of the transformer by improving the quality of the transformer insulation, ignoring the impact of the connected load on the service life of the transformer.

[0004] In the power distribution system of the inland port area, there are a large number of electric heavy trucks in the port area that are responsible for the cargo transfer between the front yard and the rear yard. When they are in the charging state, they will consume a lot of power. Due to the lack of scientific and reasonable coordination plan for the charging of electric heavy trucks in the inland port area, the access load of the transformer is not well controlled. When a high proportion of electric heavy trucks in the port area are disorderly connected to the transformer for charging, it will cause the load of the port power grid to increase sharply and bring a large amount of harmonic current, causing frequent and violent two-way fluctuations in the power flow of the transformer, thereby affecting the hot spot temperature of the transformer, and then affecting the power supply reliability and service life of the transformer, and cannot well guarantee the safety and stability of the inland port area distribution system. Summary of the invention

[0005] In order to solve the deficiencies in the prior art, the present invention provides a method and device for extending the service life of an inland port transformer. By coordinating the charging access time of each electric heavy truck and limiting the charging time, the impact on the port transformer is reduced, thereby increasing the service life of the transformer and ensuring the safety and stability of the inland port power distribution system.

[0006] The present invention adopts the following technical solution.

[0007] In a first aspect, the present invention provides a method for extending the service life of a transformer in an inland port area, the method comprising: Step S1, respectively collecting real-time current and voltage data of the lithium-ion batteries of each electric heavy-duty truck in the target port area to construct a dual-polarization equivalent circuit model of the lithium-ion batteries of each electric heavy-duty truck; Step S2, discretizing the dual-polarization equivalent circuit models of the lithium-ion batteries constructed in step S1, respectively, and establishing a discrete state space model of each lithium-ion battery, thereby obtaining the real-time state of charge of the battery of each electric heavy truck; Step S3: Based on the transformer winding hot spot temperature calculation model, establish an electric heavy truck charging scheduling model with the minimum transformer life loss as the objective function; Step S4, combining the real-time charge state of the battery of each electric heavy truck and the formulated constraints, solving the electric heavy truck charging scheduling model, and obtaining an optimized scheduling plan for the charging access time and charging duration of each electric heavy truck, so as to minimize the transformer life loss and thus extend the service life of the transformer.

[0008] Optionally, step S1 includes: using a recursive least squares algorithm based on a particle swarm algorithm to calculate the collected current and voltage data to obtain the battery ohmic internal resistance R 0 and two RC network parameter values, and based on the values ​​of each parameter, a dual-polarization equivalent circuit model of the lithium-ion battery of the electric heavy truck is constructed; Among them, two RC networks are used to describe the dynamic characteristics of lithium-ion batteries, and the parameters of the two RC networks include: respectively representing the first resistance used to describe the electrochemical polarization R 1 and the first capacitor C 1, and respectively represent the second resistance used to describe concentration polarization R 2 and the second capacitor C 2.

[0009] Optionally, the step of calculating the collected current and voltage data using a recursive least squares algorithm with an adaptive optimal forgetting factor based on a particle swarm algorithm includes: Step 1: Initialize the forgetting factor population and set the initial observation vector , initial forgetting factor , initial Kalman gain matrix and the initial covariance matrix P 0; Step 2: Use the Kalman filter algorithm to clean the real-time current and voltage data of the target electric heavy-duty truck lithium-ion battery sampled at the current time to obtain the experimental current and experimental voltage; Step 3: Based on the experimental current, the open circuit voltage value of the lithium-ion battery equivalent circuit is calculated using the ampere-hour integration method; Step 4: Combine the experimental current, experimental voltage and open circuit voltage under the current sampling to obtain the current observation vector, and combine the current forgetting factor to calculate the Kalman gain matrix , covariance matrix P and identification vector Make updates; Step 5: Calculate the current parameter identification error based on the identification vectors before and after the update ; Step 6: Set the current parameter identification error As the fitness factor in the PSO algorithm, based on the current fitness factor Calculate the individual optimal value and the population optimal value, and compare them with the previous individual optimal value and the population optimal value, and retain the better value as the current individual optimal value pBest e and the population optimal value gBest e ; Step 7: Particles are based on the current individual optimal value pBest e and the population optimal value gBest e Correct the flight attitude to update the speed and position of each particle; Step 8: Based on the updated speed and position of each particle, determine whether the inertia factor value of each particle speed is greater than 1. If so, the particle with the highest fitness value in the current particle group is taken as the optimal solution to obtain the forgetting factor of the current update. ; Otherwise, after updating the introspection factor, imitation factor and constraint factor, return to step 7 to continue correcting the flight attitude; Step 9: For the forgetting factor obtained in step 8 Update again and output the final forgetting factor of the current time ; Step 10: Determine the forgetting factor output in step 9 If the optimal value is reached, the real-time current and voltage data of the lithium-ion battery of the electric heavy truck in the target port area will be collected again, and then the process will return to step 2. If so, based on the current Kalman gain matrix , covariance matrix , identification vector and forgetting factor Calculate the first current I 1. Second current I 2. The third current I 3. Fourth current I 4. Battery ohmic internal resistance R 0; Step 11: Based on the first current I 1. Second current I 2. The third current I 3. Fourth current I 4. Battery ohmic internal resistance R 0, calculate the first resistance R 1 and the first capacitor C 1. Second resistor R 2 and the second capacitor C 2, thus constructing a dual-polarization equivalent circuit model of lithium-ion batteries.

[0010] Optionally, in step 7, the formulas for updating the velocity and position of each particle are as follows: , , In the formula, and Respectively Particle e Second and Speed ​​after update; For the e Parameter identification error after the update; f is the sequence number of the particle in the population, , is the total number of all particles; Indicates r The introspection factor at the iteration; Indicates r The imitation factor at the iteration; rand Represents a random number between (0,1); and Respectively represent e The individual optimal value and the population optimal value after the update; and Respectively Particle e Second and The updated position; Indicates r The constraint factor for the iteration.

[0011] Optionally, the update formulas of the introspection factor, the imitation factor and the constraint factor are as follows: ,

[0012] In the formula, For the e Parameter identification error after the update; and Respectively represent r +1 and r The introspection factor at the iteration; and Respectively represent r +1 and r The imitation factor at the iteration; and Indicates r +1 and r The constraint factor for the iteration.

[0013] Optionally, in step S2, the discrete state space model of the lithium-ion battery is expressed as follows: , In the formula, U oc is the open circuit voltage of the battery; For the current moment The current flowing through the battery; Indicates the current time The terminal voltage of the battery; For the current moment The terminal voltage of the first RC network; For the current moment The terminal voltage of the second RC network; For the previous moment -1 The terminal voltage of the first RC network; For the previous moment -1 The terminal voltage of the second RC network; SOC( ) is the current time of the target electric heavy truck battery State of charge; SOC ( ) is the previous moment of the target electric heavy truck battery -1 state of charge; η is the Coulomb efficiency; C N is the available capacity of the battery; M is the sampling period.

[0014] Optionally, in step S3, the transformer winding hot spot temperature calculation model adopts a temperature correction model that takes into account the influence of load factor and harmonic distortion rate, and its expression is as follows: , In the formula, For the current moment Transformer winding hot spot temperature; For the current moment Temperature rise of the inner top layer oil relative to the environment; is the temperature rise of the top oil relative to the environment under rated operating conditions; P NL is the no-load loss; For the current moment Load loss; Indicates the load loss under rated operating conditions; n Indicates the calculated index of the top oil temperature rise of the transformer; For the current moment The temperature rise of the inner winding hot spot relative to the top layer of oil; is the temperature rise of the winding hot spot relative to the top oil layer under rated operating conditions; For the previous moment Winding resistance loss; Indicates the winding resistance loss under rated operating conditions; Indicates the winding eddy current loss under rated operating conditions; For the current moment Winding eddy current loss; m Calculates the hot spot temperature rise index for transformer windings.

[0015] Optionally, in step S3, the objective function is expressed as follows: , In the formula, A Indicates the total number of charging time periods. The charging time of each electric heavy truck is divided into A Evenly spaced periods of time; Indicates the total number of all electric heavy trucks in the target port area; Indicates k Electric heavy trucks in time interval a Charging time within Indicates k Electric heavy trucks in time interval a The amount of charge in express Hot spot temperature of transformer winding at all times; Indicates the time interval a The loss of transformer life caused by all electric heavy trucks connected to the charger.

[0016] Optionally, in step S4, the formulated constraints include: uniqueness constraint of the execution equipment of a single transport task, uniqueness constraint of a single electric heavy truck executing the transport task, transport task continuity constraint, immediate transport task continuity constraint, transport task time flow constraint, charging task time constraint, uniqueness constraint of a single electric heavy truck selecting a charging pile, uniqueness constraint of transport tasks and charging tasks, electric heavy truck battery capacity constraint, and transformer winding hot spot temperature constraint.

[0017] Optionally, the electric heavy truck battery capacity constraints include: After the electric heavy truck completes the transportation task, the remaining battery power at the starting point meets the following relationship constraints: , After the electric heavy truck transport task is completed, the remaining battery power at the starting point of the charging task satisfies the following relationship constraints: , After the electric heavy truck charging task is completed, the remaining battery power at the time of initial charging meets the following relationship constraints: , The amount of electricity charged by the electric heavy truck in each time interval satisfies the following relationship constraints: , Range constraints of electric heavy truck battery power: , In the formula, Indicates electric heavy truck k exist End the task at any time i The amount of electricity when Indicates electric heavy truck k exist Start the task at any time i The amount of electricity when Indicates the power consumption per unit time when the electric heavy truck is loaded; Electric heavy truck k Carry out transportation tasks i The start time of Electric heavy truck k Carry out transportation tasks i End time of I represents a set of transport tasks, V Represents a collection of electric heavy trucks; Indicates electric heavy truck k exist Always start charging tasks b The amount of electricity when Indicates the power consumption per unit time when the electric heavy truck is idling; Electric heavy truck k Perform charging tasks b The start time of is a positive integer; The value is 0 or 1. When the value is 1, it indicates an electric heavy truck. k Complete the task i Then proceed to perform the charging task b , the rest of the states are 0; B Represents a charging task set; Indicates electric heavy truck k exist Always finish charging tasks b The amount of electricity when Indicates the charging rate of the charging pile, that is, the amount of charge per unit time s ; Electric heavy truck k Perform charging tasks b End time of For the k Electric heavy trucks in time interval a The amount of electricity charged in the Indicates the minimum threshold of the electric heavy truck's power. Indicates the maximum threshold of the electric heavy truck's power.

[0018] Optionally, the transformer winding hot spot temperature constraint is as follows: , In the formula, express The hot spot temperature of the transformer winding at any moment, Indicates the maximum threshold of the hot spot temperature of the transformer winding; when the hot spot temperature of the transformer winding exceeds the threshold, the charging tasks of the corresponding electric heavy trucks are interrupted in order from large to small in the order of the remaining power of the electric heavy battery, so that the hot spot temperature of the transformer winding is maintained below the threshold.

[0019] In a second aspect, the present invention provides a device for extending the service life of a transformer in an inland port area, which performs the steps of any method described in the first aspect of the present invention, and the device comprises: The acquisition module is used to respectively collect the real-time current and voltage data of the lithium-ion batteries of each electric heavy-duty truck in the target port area, so as to construct a dual-polarization equivalent circuit model of the lithium-ion batteries of each electric heavy-duty truck; The discretization module is used to discretize the constructed dual-polarization equivalent circuit models of each lithium-ion battery, establish a discrete state space model of each lithium-ion battery, and thus obtain the real-time charge state of the battery of each electric heavy truck; Establish a target module to establish an electric heavy truck charging scheduling model with the minimum transformer life loss as the objective function based on the transformer winding hot spot temperature calculation model; The solution optimization module is used to solve the charging scheduling model of the electric heavy trucks in combination with the real-time charge state of the batteries of each electric heavy truck and the formulated constraints, and obtain the optimal scheduling plan for the charging access time and charging duration of each electric heavy truck, so as to minimize the life loss of the transformer and thus extend the service life of the transformer.

[0020] In a third aspect, the present invention provides a terminal, including a processor and a storage medium; The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the steps of any one of the methods described in the first aspect of the present invention.

[0021] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the methods described in the first aspect of the present invention.

[0022] The beneficial effects of the present invention are as follows: 1. The present invention fully considers the relationship between the real-time state of charge (SOC) of the battery of an electric heavy truck and the transformer load rate; when the battery SOC is low, the electric heavy truck needs to obtain more electric energy from the power grid to replenish the power, resulting in an increase in the transformer load rate; and when the battery SOC is high, the charging demand is small, and the transformer load rate is low at this time; therefore, the present invention obtains the real-time state of charge of the battery of each electric heavy truck by constructing a discrete state space model of each lithium-ion battery, so as to facilitate scientific and reasonable coordination of the charging strategies of each electric heavy truck.

[0023] 2. The present invention takes into account that the charging process of electric heavy trucks will cause the load rate of the transformer to increase, and the connection process and charging process will cause the harmonic distortion rate of the current in the power grid to increase, and both will cause the heat generation of the transformer to increase, accelerate the aging process of the transformer, and further affect the life loss of the transformer; therefore, the present invention takes into account the additional loss of the port area transformer caused by the load rate and harmonic distortion rate generated when the port area electric heavy trucks are connected to the transformer for charging, and establishes an electric heavy truck charging scheduling model with the minimum transformer life loss as the objective function based on the transformer winding hot spot temperature calculation model. This model can achieve the goal of coordinating the charging time and charging capacity of each electric heavy truck, reducing its impact on the transformer, reducing the hot spot temperature of the transformer winding, thereby increasing the service life of the transformer, ensuring the safety and stability of the inland port area power distribution system, and improving the utilization efficiency of equipment and energy.

[0024] 3. The present invention adopts a dual-polarization equivalent circuit model to describe the electrochemical characteristics of lithium-ion power batteries during operation, and integrates the adaptive optimal forgetting factor recursive least squares algorithm based on the particle swarm algorithm to further improve the identification accuracy of battery parameters. By optimizing the imitation factor, introspection factor and constraint factor in the particle swarm algorithm, the problem that traditional algorithms are prone to fall into local optimality is solved, and the solution accuracy and convergence speed of the model are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic diagram of the process of extending the service life of a transformer in an inland port area in the present invention; Figure 2 It is a logical schematic diagram of the PSO-FFRLS algorithm in the present invention; Figure 3 This is a schematic diagram of the circuit structure of a dual-polarization model of a lithium-ion battery for an electric heavy truck; Figure 4 This is a schematic diagram of the cumulative life loss of transformers in the port area in January in the experiment of the present invention; Figure 5 It is a schematic diagram of the cumulative life loss of the transformer in the port area in August in the present invention; Figure 6 The present invention is a block diagram of the structural principle of the device for extending the service life of an inland port transformer. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in the present invention are only part of the embodiments of the present invention, not all of the embodiments. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0027] Embodiment 1: Reference Figure 1 The embodiment of the present invention provides a method for extending the service life of a transformer in an inland port area, which specifically includes the following steps: Step S1, respectively collecting real-time current and voltage data of the lithium-ion batteries of each electric heavy-duty truck in the target port area to construct a dual-polarization equivalent circuit model of the lithium-ion batteries of each electric heavy-duty truck; Step S2, discretizing the dual-polarization equivalent circuit models of the lithium-ion batteries constructed in step S1, respectively, and establishing a discrete state space model of each lithium-ion battery, thereby obtaining the real-time state of charge of the battery of each electric heavy truck; Step S3: Based on the transformer winding hot spot temperature calculation model, establish an electric heavy truck charging scheduling model with the minimum transformer life loss as the objective function; Step S4, combining the real-time charge state of the battery of each electric heavy truck and the formulated constraints, solving the electric heavy truck charging scheduling model, and obtaining an optimized scheduling plan for the charging access time and charging duration of each electric heavy truck, so as to minimize the transformer life loss and thus extend the service life of the transformer.

[0028] Specifically, in step S1 of this embodiment, the collected current and voltage data are calculated using a particle swarm algorithm-based adaptive optimal forgetting factor recursive least squares algorithm (PSO-FFRLS algorithm) to obtain the battery ohmic internal resistance R 0 and two RC network parameter values, and based on the values ​​of each parameter, a dual-polarization equivalent circuit model of the lithium-ion battery of the electric heavy truck is constructed.

[0029] As an embodiment of the present invention, refer to Figure 2 In step S1, the steps of using the PSO-FFRLS algorithm to calculate and obtain the parameters of the lithium-ion battery equivalent circuit components including the battery ohmic internal resistance and the values ​​of two RC network parameters include: Step 1: Initialize the forgetting factor population and set the initial observation vector , initial forgetting factor , initial Kalman gain matrix and the initial covariance matrix P 0; In this embodiment, the initialization value of each parameter is: =0.99; =[0 0 0 0 0 0]; P 0=10 5 ; Step 2: Use the Kalman filter algorithm to clean the real-time current and voltage data of the target electric heavy-duty truck lithium-ion battery sampled at the current time to obtain the experimental current and experimental voltage; Step 3: Based on the experimental current, the open circuit voltage value of the lithium-ion battery equivalent circuit is calculated using the ampere-hour integration method; Step 4: Combine the experimental current, experimental voltage and open-circuit voltage under the current sampling to obtain the current observation vector, and update the Kalman gain matrix, covariance matrix and identification vector in combination with the current forgetting factor; the update formula is as follows: , In the formula, and Respectively e Second and The identification vector after the update; and Respectivelye Second and The updated Kalman gain matrix; and Respectively e Second and The updated covariance matrix; For the The forgetting factor after the update; and Respectively e Second and The observation vector after the update; For the e The experimental current at the time of sampling; M is the sampling period. M =0.1 s .

[0030] Step 5: Calculate the current parameter identification error based on the identification vectors before and after the update. The calculation formula is as follows: , In the formula, For the e The parameter identification error after the update is and Respectively e Second and The updated identification vector.

[0031] Step 6: Set the current parameter identification error As the fitness factor in the PSO algorithm, based on the current fitness factor Calculate the individual optimal value and the population optimal value, and compare them with the previous individual optimal value and the population optimal value, and retain the better value as the current individual optimal value pBest e and the population optimal value gBest e .

[0032] Step 7: Particles are based on the current individual optimal value pBest e and the population optimal value gBest e Correct the flight attitude and update the speed and position of each particle; the update formulas are as follows: , , In the formula, and Respectively Particle e Second and Speed ​​after update; and Respectively Particle e Second and The updated position; f is the serial number of the particle in the population, ranging from , is the total number of all particles; Indicates that when the PSO algorithm termination condition is not met, the algorithm will be updated in a loop to the first r The introspection factor at the iteration time reflects the self-learning ability of the particle. The initial value of is 2; Indicates that when the PSO algorithm termination condition is not met, the algorithm will be updated in a loop to the first r The imitation factor at this time reflects the social learning ability of the particle. In this embodiment, The initial value of is 2; rand Represents a random number between (0,1); Indicates that when the PSO algorithm termination condition is not met, the algorithm will be updated in a loop to the first r The constraint factor at the iteration is the constraint on the speed. The initial value of is 1; and Respectively represent e The individual optimal value and the population optimal value after the update; Step 8: Based on the updated speed and position of each particle, determine whether the inertia factor value of each particle speed is greater than 1. If so, the particle with the highest fitness value in the current particle group is taken as the optimal solution to obtain the forgetting factor of the current update. ; Otherwise, after updating the introspection factor, imitation factor and constraint factor, return to step 7 to continue correcting the flight attitude; It is further explained that the introspection factor and imitation factor The influence of the individual experience information of the particle and the experience information of other particles on the optimization trajectory reflects the information interaction characteristics between particles, while the constraint factor This limits the speed of the particles. A large value will lead to insufficient exploration of particles within the global search range; A large value may cause particles to converge to the local optimal solution prematurely; The value may cause particles to move too fast in the early stage, making the exploration activities difficult to control. Therefore, in the initial search stage of the algorithm, a larger Value, smaller Value and smaller value, so that particles can diverge as much as possible in the search space, that is, emphasize "individual independent consciousness", and be less affected by other particles in the population, that is, "social consciousness part", so as to increase the diversity of particles in the group. Gradually decreasing, Gradually increasing, Gradually increasing, such an adjustment strategy enhances the ability of particles to converge to the local optimal solution. In order to meet more flexible search requirements, a preferred but non-limiting embodiment of the present invention is to adaptively select the introspection factor , imitation factor and constraint factors The update formulas are as follows: , In the formula, For the e Parameter identification error after the update; and Respectively represent r +1 and r The introspection factor at the iteration; and Respectively represent r +1 and r The imitation factor at the iteration; and Indicates r +1 and r The constraint factor for the iteration.

[0033] Step 9: For the forgetting factor obtained in step 8 Update again and output the final forgetting factor of the current time ; The update formula is as follows: , In the formula, is the forgetting factor The optimal forgetting factor after updating.

[0034] Step 10: Determine whether the optimal forgetting factor updated in step 9 reaches the set optimal value (the optimal value is set to 0.98 in this embodiment). If not, re-collect the real-time current and voltage data of the lithium-ion battery of the electric heavy truck in the target port area, and return to step 2; if so, based on the current Kalman gain matrix , covariance matrix , identification vector and the optimal forgetting factor Calculate the first current I 1. Second current I 2. The third current I3. Fourth current I 4. Battery ohmic internal resistance R 0: , In the formula, .

[0035] Step 11: Based on the first current I 1. Second current I 2. The third current I 3. Fourth current I 4. Battery ohmic internal resistance R 0, calculate the first resistance R 1 and the first capacitor C 1. Second resistor R 2 and the second capacitor C 2, thus constructing a dual-polarization equivalent circuit model of lithium-ion batteries.

[0036] .

[0037] The circuit structure of the lithium-ion battery dual polarization equivalent circuit model is as follows: Figure 3 As shown in the figure U oc Indicates the open circuit voltage of the power battery. This parameter is related to the state of charge and temperature. R 0 represents the ohmic internal resistance of the power battery, which is related to the material selection of the current collector, electrode and electrolyte and the degree of battery aging; two RC networks are used to describe the dynamic characteristics of the battery. R 1 and C 1 represent the first resistance and the first capacitance used to describe the electrochemical polarization, R 2 and C 2 represents the second resistor and the second capacitor used to describe concentration polarization. The more RC networks there are, the better the model accuracy and the higher the complexity. I represents the current flowing through the battery, U Indicates the terminal voltage of the battery.

[0038] Furthermore, in step S2, the continuous sampling period of the established lithium-ion battery dual-polarization equivalent circuit model is divided into multiple discrete time intervals, and the original circuit model is discretized using bilinear transformation, and finally a discrete state space model of the lithium-ion battery used in the electric heavy truck is established to obtain the real-time state of charge of the battery of the target electric heavy truck. The expression of the discrete state space model of the lithium-ion battery is as follows: , In the formula, U oc is the open circuit voltage of the battery; For the current moment The current flowing through the battery; Indicates the current time The terminal voltage of the battery; For the current moment The terminal voltage of the first RC network; For the current moment The terminal voltage of the second RC network; For the previous moment -1 The terminal voltage of the first RC network (V); For the previous moment -1 The terminal voltage of the second RC network (V); SOC ( ) is the current time of the target electric heavy truck battery State of charge; SOC ( ) is the previous moment of the target electric heavy truck battery -1 state of charge; η is the Coulomb efficiency; C N is the available capacity of the battery (Ah); M is the sampling period.

[0039] In a preferred but non-limiting embodiment, the transformer winding hot spot temperature calculation model in step S3 adopts a temperature correction model that takes into account the influence of load factor and harmonic distortion rate, and its expression is as follows: , In the formula, For the current moment Transformer winding hot spot temperature; For the current moment Temperature rise of the inner top layer oil relative to the environment; is the temperature rise of the top oil relative to the environment under rated operating conditions; P NL is the no-load loss; For the current moment Load loss; Indicates the load loss under rated operating conditions; n Indicates the calculated index of the top oil temperature rise of the transformer; For the current moment The temperature rise of the inner winding hot spot relative to the top layer of oil; is the temperature rise of the winding hot spot relative to the top oil layer under rated operating conditions; For the previous moment Winding resistance loss; Indicates the winding resistance loss under rated operating conditions; Indicates the winding eddy current loss under rated operating conditions; For the current moment Winding eddy current loss; m Calculates the hot spot temperature rise index for transformer windings.

[0040] Among them, the load loss and winding resistance losses The calculation formula is as follows: , , , , , , , In the formula, h is the harmonic current order, Indicates the upper limit of the harmonic current order taken for calculation; For harmonic current, each harmonic current value can be obtained by Fourier decomposition of the collected real-time current; is the harmonic loss factor of the winding resistance; is the winding eddy current harmonic loss factor; is the stray harmonic loss factor; is the load factor of the transformer; is the stray harmonic loss under rated operating conditions; Indicates the fundamental current.

[0041] The present invention takes into account the real-time charge state of the battery of the electric heavy truck and the additional loss of the service life of the port transformer caused by the load rate and harmonic distortion rate when the port electric heavy truck is connected to the power grid for charging. In step S3, the following objective function is proposed with the goal of minimizing the loss of transformer life: , In the formula, A Indicates the total number of charging time periods. The charging time of each electric heavy truck is divided into A Evenly spaced periods of time; Indicates the total number of all electric heavy trucks in the target port area; Indicates k Electric heavy trucks in time interval a Charging time within Indicates k Electric heavy trucks in time interval a The amount of charge in express Hot spot temperature of transformer winding at all times; Indicates the time interval a The loss of transformer life caused by all electric heavy trucks connected to the charger.

[0042] As an embodiment of the present invention, in step S4, the constraints formulated include: (1) Uniqueness constraint of the execution equipment of a single transport task: that is, each task can only be executed by one electric heavy truck through the following constraint: , In the formula, I represents a set of transport tasks, ; ; ; V Represents a collection of electric heavy trucks. .

[0043] (2) Uniqueness constraint for a single electric heavy truck to perform a transport task: That is, the following constraint is used to ensure that the electric heavy truck does not participate in the execution of other tasks after the current transport task starts and before the current transport task ends: , In the formula, , represents the set of transportation tasks after adding two virtual tasks; N +1 means i After a task starts, the virtual task that is assumed to start, N +2 means the i Before the end of a task, the virtual task assumed to be started has a virtual operation time of 0.

[0044] (3) Transport task continuity constraint: that is, each transport task is constrained to have only one immediate predecessor task and immediate successor task through the following formula: .

[0045] (4) Continuity constraint of the transport task: that is, the transport task shall not start earlier than the specified time through the following constraint: , In the formula, Electric heavy truck k Carry out transportation tasks i The start time of The earliest transport task start time.

[0046] (5) Transport task time flow constraint: that is, the electric heavy truck completes the transport task i When the next transport task is executed (i.e. and )’s time constraints are as follows: , In the formula, Electric heavy truck k Carry out transportation tasks i End time of Electric heavy truck k Carry out transportation tasks j The start time of For transportation tasks i With transport tasks j the time interval between B represents a charging task set, B ={ N +3, N +4,…, b}, .

[0047] (6) Charging task time constraints include: When the electric heavy truck performs the charging task after completing the transportation task (i.e. when ,and or )’s time constraints are as follows: , In the formula, Electric heavy truck k Perform charging tasks b The start time of For charging tasks b With transport tasks j the time interval between ; , The charging end time constraints for electric heavy trucks are as follows: , In the formula, Electric heavy truck k Perform charging tasks b End time of The minimum charging time for electric heavy trucks.

[0048] The total charging time constraints for electric heavy trucks are as follows: , In the formula, Electric heavy truck k In charging task b The total charging time in.

[0049] (7) Unique constraint for selecting a charging station for a single electric heavy truck: That is, the following constraint is used to constrain that an electric truck can only be charged at one charging station: , In the formula, , (8) Uniqueness constraint of transport and charging tasks: That is, an electric heavy truck cannot perform both transport and charging tasks at the same time through the following constraint: , (9) Battery capacity constraints for electric heavy trucks: After the electric heavy truck completes the transportation task, the remaining battery power at the starting point meets the following relationship constraints: , After the electric heavy truck transport task is completed, the remaining battery power at the starting point of the charging task satisfies the following relationship constraints: , After the electric heavy truck charging task is completed, the remaining battery power at the time of initial charging meets the following relationship constraints: , The amount of electricity charged by the electric heavy truck in each time interval satisfies the following relationship constraints: , Range constraints of electric heavy truck battery power: , In the formula, Indicates electric heavy truck k exist End the task at any time i The power of Indicates electric heavy truck k exist Start the task at any time i The power of Indicates the power consumption per unit time when the electric heavy truck is loaded, in units of kWh / s ; Electric heavy truck k Carry out transportation tasks i The start time of Electric heavy truck k Carry out transportation tasks i End time of I represents a set of transport tasks, V Represents a collection of electric heavy trucks; Indicates electric heavy truck k exist Always start charging tasks bThe power of Indicates the power consumption per unit time when the electric heavy truck is idling, in units of kWh / s ; Electric heavy truck k Perform charging tasks b The start time of is a positive integer, and in this embodiment, the value is 10 8 ; , B Represents a charging task set; Indicates electric heavy truck k exist Always finish charging tasks b The power of Indicates the charging rate of the charging pile, that is, the amount of charge per unit time, in units of kWh / s ; Electric heavy truck k Perform charging tasks b End time of For the k Electric heavy trucks in time interval a The amount of electricity charged in the Indicates the minimum threshold of the electric heavy truck's power. Indicates the maximum threshold of the electric heavy truck's power.

[0050] (10) Transformer winding hot spot temperature constraints: , In the formula, express The hot spot temperature of the transformer winding at any moment, Indicates the maximum threshold of the hot spot temperature of the transformer winding; when the hot spot temperature of the transformer winding exceeds the threshold, the charging tasks of the corresponding electric heavy trucks are interrupted in order from large to small in the order of the remaining power of the electric heavy battery, so that the hot spot temperature of the transformer winding is maintained below the threshold.

[0051] Furthermore, the total power consumption of all electric heavy trucks in the port area is calculated as follows: , As the proportion of electric heavy trucks connected to the port power grid increases, the load on the transformer gradually increases. The following experimental analysis is conducted on the data of January and August in an inland port area to verify the effectiveness of the method for extending the service life of transformers in the inland port area proposed by the present invention. Figure 4 and Figure 5As shown, the cumulative life loss graphs of the port area transformers in January and August are obtained respectively; due to the lower temperature in January, the total amount of cargo in the port area has decreased significantly compared to August, resulting in a decrease in the charging frequency of electric heavy trucks. Compared with the transformer in January, the transformer in August is in a heavy-load operation state. From the comparative experimental results in the figure, it can be seen that the optimized scheduling of charging access time and charging duration of electric heavy trucks based on the electric heavy truck charging scheduling model proposed in the present invention can effectively reduce the life loss of transformers, which has a positive effect on the operation of transformers in the port area. Thereby extending the service life of the transformer, The beneficial effects of the present invention are as follows: 1. The present invention fully considers the relationship between the real-time state of charge (SOC) of the battery of an electric heavy truck and the transformer load rate; when the battery SOC is low, the electric heavy truck needs to obtain more electric energy from the power grid to replenish the power, resulting in an increase in the transformer load rate; and when the battery SOC is high, the charging demand is small, and the transformer load rate is low at this time; therefore, the present invention obtains the real-time state of charge of the battery of each electric heavy truck by constructing a discrete state space model of each lithium-ion battery, so as to facilitate scientific and reasonable coordination of the charging strategies of each electric heavy truck.

[0052] 2. The present invention takes into account that the charging process of electric heavy trucks will cause the load rate of the transformer to increase, and the connection process and charging process will cause the harmonic distortion rate of the current in the power grid to increase, and both will cause the heat generation of the transformer to increase, accelerate the aging process of the transformer, and further affect the life loss of the transformer; therefore, the present invention takes into account the additional loss of the port area transformer caused by the load rate and harmonic distortion rate generated when the port area electric heavy trucks are connected to the transformer for charging, and establishes an electric heavy truck charging scheduling model with the minimum transformer life loss as the objective function based on the transformer winding hot spot temperature calculation model. This model can achieve the goal of coordinating the charging time and charging capacity of each electric heavy truck, reducing its impact on the transformer, reducing the hot spot temperature of the transformer winding, thereby increasing the service life of the transformer, ensuring the safety and stability of the inland port area power distribution system, and improving the utilization efficiency of equipment and energy.

[0053] 3. The present invention adopts a dual-polarization equivalent circuit model to describe the electrochemical characteristics of lithium-ion power batteries during operation, and integrates the adaptive optimal forgetting factor recursive least squares algorithm based on the particle swarm algorithm to further improve the identification accuracy of battery parameters. By optimizing the imitation factor, introspection factor and constraint factor in the particle swarm algorithm, the problem that traditional algorithms are prone to fall into local optimality is solved, and the solution accuracy and convergence speed of the model are improved.

[0054] Embodiment 2: like Figure 6As shown, the present invention provides a device for extending the service life of a transformer in an inland port area, the device is used to implement the steps of the method in the above embodiment 1, and the device specifically includes: The acquisition module is used to respectively collect the real-time current and voltage data of the lithium-ion batteries of each electric heavy-duty truck in the target port area, so as to construct a dual-polarization equivalent circuit model of the lithium-ion batteries of each electric heavy-duty truck; The discretization module is used to discretize the constructed dual-polarization equivalent circuit models of each lithium-ion battery, establish a discrete state space model of each lithium-ion battery, and thus obtain the real-time charge state of the battery of each electric heavy truck; Establish a target module to establish an electric heavy truck charging scheduling model with the minimum transformer life loss as the objective function based on the transformer winding hot spot temperature calculation model; The solution optimization module is used to solve the charging scheduling model of the electric heavy trucks in combination with the real-time charge state of the batteries of each electric heavy truck and the formulated constraints, and obtain the optimal scheduling plan for the charging access time and charging duration of each electric heavy truck, so as to minimize the life loss of the transformer and thus extend the service life of the transformer.

[0055] The device for extending the service life of an inland port transformer provided in the embodiment of the present invention and the method for extending the service life of an inland port transformer provided in Example 1 are based on the same technical concept, and can produce the beneficial effects as described in Example 1. For the contents not described in detail in this embodiment, please refer to Example 1.

[0056] Embodiment three: A terminal provided by an embodiment of the present invention includes a processor and a storage medium; The storage medium is used to store instructions; The processor is used to operate according to the instruction to execute the steps of the method according to any one of the first embodiments.

[0057] Embodiment 4: An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps of any method described in Embodiment 1 are implemented.

[0058] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0059] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the above. The computer-readable storage medium used herein is not to be interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0060] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0061] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect through the Internet). In some embodiments, by using the state information of the computer-readable program instructions to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit may execute the computer-readable program instructions, thereby implementing various aspects of the present disclosure.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for extending the service life of transformers in inland port areas, characterized in that: Methods include: Step S1, respectively collecting real-time current and voltage data of the lithium-ion batteries of each electric heavy-duty truck in the target port area to construct a dual-polarization equivalent circuit model of the lithium-ion batteries of each electric heavy-duty truck; Step S2, discretizing the dual-polarization equivalent circuit models of the lithium-ion batteries constructed in step S1, respectively, and establishing a discrete state space model of each lithium-ion battery, thereby obtaining the real-time state of charge of the battery of each electric heavy truck; Step S3: Based on the transformer winding hot spot temperature calculation model, establish an electric heavy truck charging scheduling model with the minimum transformer life loss as the objective function; Step S4, combining the real-time charge state of the battery of each electric heavy truck and the formulated constraints, solving the electric heavy truck charging scheduling model, and obtaining an optimized scheduling plan for the charging access time and charging duration of each electric heavy truck, so as to minimize the transformer life loss and thus extend the service life of the transformer.

2. The method for extending the service life of an inland port transformer according to claim 1, characterized in that: The step S1 includes: using a particle swarm algorithm-based adaptive optimal forgetting factor recursive least squares algorithm to calculate the collected current and voltage data to obtain the battery ohmic internal resistance R 0 and two RC network parameter values, and based on the values ​​of each parameter, a dual-polarization equivalent circuit model of the lithium-ion battery of the electric heavy truck is constructed; Among them, two RC networks are used to describe the dynamic characteristics of lithium-ion batteries, and the parameters of the two RC networks include: respectively representing the first resistance used to describe the electrochemical polarization R 1 and the first capacitor C 1, and respectively represent the second resistance used to describe concentration polarization R 2 and the second capacitor C 2.

3. The method for extending the service life of transformers in inland port areas according to claim 2, characterized in that: The step of calculating the collected current and voltage data using a recursive least squares algorithm with an adaptive optimal forgetting factor based on a particle swarm algorithm includes: Step 1: Initialize the forgetting factor population and set the initial observation vector , initial forgetting factor , initial Kalman gain matrix and the initial covariance matrix P 0; Step 2: Use the Kalman filter algorithm to clean the real-time current and voltage data of the target electric heavy-duty truck lithium-ion battery sampled at the current time to obtain the experimental current and experimental voltage; Step 3: Based on the experimental current, the open circuit voltage value of the lithium-ion battery equivalent circuit is calculated using the ampere-hour integration method; Step 4: Combine the experimental current, experimental voltage and open circuit voltage under the current sampling to obtain the current observation vector, and combine the current forgetting factor to calculate the Kalman gain matrix , covariance matrix P and identification vector Make updates; Step 5: Calculate the current parameter identification error based on the identification vectors before and after the update ; Step 6: Set the current parameter identification error As the fitness factor in the PSO algorithm, based on the current fitness factor Calculate the individual optimal value and the population optimal value, and compare them with the previous individual optimal value and the population optimal value, and retain the better value as the current individual optimal value pBest e and the population optimal value gBest e ; Step 7: Particles are based on the current individual optimal value pBest e and the population optimal value gBest e Correct the flight attitude to update the speed and position of each particle; Step 8: Based on the updated speed and position of each particle, determine whether the inertia factor value of each particle speed is greater than 1. If so, the particle with the highest fitness value in the current particle group is taken as the optimal solution to obtain the forgetting factor of the current update. ; Otherwise, after updating the introspection factor, imitation factor and constraint factor, return to step 7 to continue correcting the flight attitude; Step 9: For the forgetting factor obtained in step 8 Update again and output the final forgetting factor of the current time ; Step 10: Determine the forgetting factor output in step 9 If the optimal value is reached, the real-time current and voltage data of the lithium-ion battery of the electric heavy truck in the target port area will be collected again, and then the process will return to step 2. If so, based on the current Kalman gain matrix , covariance matrix , identification vector and the forgetting factor Calculate the first current I 1. Second current I 2. The third current I 3. Fourth current I 4. Battery ohmic internal resistance R 0; Step 11: Based on the first current I 1. Second current I 2. The third current I 3. Fourth current I 4. Battery ohmic internal resistance R 0, calculate the first resistance R 1 and the first capacitor C 1. Second resistor R 2 and the second capacitor C 2, thus constructing a dual-polarization equivalent circuit model of lithium-ion batteries.

4. The method for extending the service life of an inland port transformer according to claim 3, characterized in that: In step 7, the formulas for updating the velocity and position of each particle are as follows: ， ， In the formula, and Respectively Particle e Second and Speed ​​after update; For the e Parameter identification error after the update; f is the sequence number of the particle in the population, , is the total number of all particles; Indicates r The introspection factor at the iteration; Indicates r The imitation factor at the iteration; rand Represents a random number between (0,1); and Respectively represent e The individual optimal value and the population optimal value after the update; and Respectively Particle e Second and The position after the update; Indicates r The constraint factor for the iteration.

5. The method for extending the service life of an inland port transformer according to claim 3 or 4, characterized in that: The update formulas of the introspection factor, imitation factor and constraint factor are as follows: ， In the formula, For the e Parameter identification error after the update; and Respectively represent r +1 and r The introspection factor at the iteration; and Respectively represent r +1 and r The imitation factor at the iteration; and Indicates r +1 and r The constraint factor for the iteration.

6. The method for extending the service life of transformers in inland port areas according to claim 2, characterized in that: In step S2, the discrete state space model of the lithium-ion battery is expressed as follows: ， In the formula, U oc is the open circuit voltage of the battery; For the current moment The current flowing through the battery; Indicates the current time The terminal voltage of the battery; For the current moment The terminal voltage of the first RC network; For the current moment The terminal voltage of the second RC network; For the previous moment -1 The terminal voltage of the first RC network; For the previous moment -1 The terminal voltage of the second RC network; SOC( ) is the current time of the target electric heavy truck battery State of charge; SOC ( ) is the previous moment of the target electric heavy truck battery -1 state of charge; η is the Coulomb efficiency; C N is the available capacity of the battery; M is the sampling period.

7. The method for extending the service life of transformers in inland port areas according to claim 1, characterized in that: In step S3, the transformer winding hot spot temperature calculation model adopts a temperature correction model that takes into account the influence of load factor and harmonic distortion rate, and its expression is as follows: ， In the formula, For the current moment Transformer winding hot spot temperature; For the current moment Temperature rise of the inner top layer oil relative to the environment; is the temperature rise of the top oil relative to the environment under rated operating conditions; P NL is the no-load loss; For the current moment Load loss; Indicates the load loss under rated operating conditions; n Indicates the calculated index of the top oil temperature rise of the transformer; For the current moment The temperature rise of the inner winding hot spot relative to the top layer of oil; is the temperature rise of the winding hot spot relative to the top oil layer under rated operating conditions; For the previous moment Winding resistance loss; Indicates the winding resistance loss under rated operating conditions; Indicates the winding eddy current loss under rated operating conditions; For the current moment Winding eddy current loss; m Calculates the hot spot temperature rise index for transformer windings.

8. The method for extending the service life of transformers in inland port areas according to claim 7, characterized in that: In step S3, the expression of the objective function is as follows: ， In the formula, A Indicates the total number of charging time periods. The charging time of each electric heavy truck is divided into A Evenly spaced periods of time; Indicates the total number of all electric heavy trucks in the target port area; Indicates k Electric heavy trucks in time interval a Charging time within Indicates k Electric heavy trucks in time interval a The amount of charge in express Hot spot temperature of transformer winding at all times; Indicates the time interval a The loss of transformer life caused by all electric heavy trucks connected to the charger.

9. The method for extending the service life of an inland port transformer according to claim 1, characterized in that: In step S4, the formulated constraints include: the uniqueness constraint of the execution equipment of a single transport task, the uniqueness constraint of a single electric heavy truck executing a transport task, the continuity constraint of the transport task, the continuity constraint of the immediate transport task, the time flow constraint of the transport task, the time constraint of the charging task, the uniqueness constraint of the selection of a charging pile by a single electric heavy truck, the uniqueness constraint of the transport task and the charging task, the battery capacity constraint of the electric heavy truck, and the hot spot temperature constraint of the transformer winding.

10. The method for extending the service life of transformers in inland port areas according to claim 9, characterized in that: The battery capacity constraints of the electric heavy truck include: After the electric heavy truck completes the transportation task, the remaining battery power at the starting point meets the following relationship constraints: ， After the electric heavy truck transport task is completed, the remaining battery power at the starting point of the charging task satisfies the following relationship constraints: ， After the electric heavy truck charging task is completed, the remaining battery power at the time of initial charging meets the following relationship constraints: ， The amount of electricity charged by the electric heavy truck in each time interval satisfies the following relationship constraints: ， Range constraints of electric heavy truck battery power: ， In the formula, Indicates electric heavy truck k exist End the task at any time i The amount of electricity when Indicates electric heavy truck k exist Start the task at any time i The amount of electricity when Indicates the power consumption per unit time when the electric heavy truck is loaded; Electric heavy truck k Carry out transportation tasks i The start time of Electric heavy truck k Carry out transportation tasks i End time of I represents a set of transport tasks, V Represents a collection of electric heavy trucks; Indicates electric heavy truck k exist Always start charging tasks b The amount of electricity when Indicates the power consumption per unit time when the electric heavy truck is idling; Electric heavy truck k Perform charging tasks b The start time of is a positive integer; The value is 0 or 1. When the value is 1, it indicates an electric heavy truck. k Complete the task i Then proceed to perform the charging task b , the rest of the states are set to 0; B Represents a charging task set; Indicates electric heavy truck k exist Always finish charging tasks b The amount of electricity when Indicates the charging rate of the charging pile, that is, the amount of charge per unit time s ; Electric heavy truck k Perform charging tasks b End time of For the k Electric heavy trucks in time interval a The amount of electricity charged in the Indicates the minimum threshold of the electric heavy truck's power. Indicates the maximum threshold of the electric heavy truck's power.

11. The method for extending the service life of an inland port transformer according to claim 9, characterized in that: The transformer winding hot spot temperature constraints are as follows: ， In the formula, express The hot spot temperature of the transformer winding at any moment, Indicates the maximum threshold of the hot spot temperature of the transformer winding; when the hot spot temperature of the transformer winding exceeds the threshold, the charging tasks of the corresponding electric heavy trucks are interrupted in order from large to small in the order of the remaining power of the electric heavy battery, so that the hot spot temperature of the transformer winding is maintained below the threshold.

12. A device for extending the service life of an inland port transformer, operating the method for extending the service life of an inland port transformer as claimed in any one of claims 1 to 11, characterized in that: The device includes: The acquisition module is used to respectively collect the real-time current and voltage data of the lithium-ion batteries of each electric heavy-duty truck in the target port area, so as to construct a dual-polarization equivalent circuit model of the lithium-ion batteries of each electric heavy-duty truck; The discretization module is used to discretize the constructed dual-polarization equivalent circuit models of each lithium-ion battery, establish a discrete state space model of each lithium-ion battery, and thus obtain the real-time charge state of the battery of each electric heavy truck; Establish a target module to establish an electric heavy truck charging scheduling model with the minimum transformer life loss as the objective function based on the transformer winding hot spot temperature calculation model; The solution optimization module is used to solve the charging scheduling model of the electric heavy trucks in combination with the real-time charge state of the batteries of each electric heavy truck and the formulated constraints, and obtain the optimal scheduling plan for the charging access time and charging duration of each electric heavy truck, so as to minimize the life loss of the transformer and thus extend the service life of the transformer.

13. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-11.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 11 are implemented.

Images